一种针对抖动无人机视频的运动目标检测算法

A Moving Object Detection Algorithm Aiming at Jittery Drone Videos

针对悬浮无人机在运动目标检测中易受抖动影响,导致大量背景噪声的问题,提出一种多尺度的EA-KDE背景差分算法(MEA-KDE)。该算法首先对图像序列进行多尺度分解,以获取多尺度的图像序列。然后,在进行检测之前,通过考虑面积阈值和当前图像帧,计算并更新检测的分割阈值,引入当前图像帧信息。其次,对不同尺度的图像帧采用高低双分割阈值进行背景差分运算,以提高检测的鲁棒性。最后,通过对各尺度的检测结果采用一种自顶向下的融合策略进行融合,以在保留目标的清晰轮廓同时消除噪声。此外,提出的一种边界扩展融合后处理算法有助于减轻检测断裂引起的目标破碎现象。实验结果表明,所提算法能够有效抑制抖动导致的背景噪声。在2个真实拍摄的无人机数据集上,分别获得了0.951和0.952的平均F1分数,相较于原算法分别提高了0.144和0.276。

To solve the problem that moving object detection is susceptible to jitter in hovering drones,leading to the generation of a significant amount of background noise and lower accuracy,a multiscale EA-KDE(MEA-KDE)background difference algorithm is proposed.This algorithm initially achieves a multiscale decomposition of image sequences to obtain a multiscale image sequence.Subsequently,before performing detection,the segmentation threshold for detection is calculated and updated by considering the area threshold and the current image frame,thereby incorporating information from the current frame.Background difference operations using high and low dual segmentation thresholds are performed on images at different scales to enhance detection robustness.Finally,a top-down fusion strategy is employed to merge the detection results from various scales,preserving the clear contours of the targets while eliminating noise.Furthermore,a proposed boundary expansion fusion post-processing algorithm helps alleviate the fragmented targets caused by detection breaks.Experimental results demonstrate that the proposed algorithm effectively suppresses background noise caused by jitter.On two real drone datasets,average F1 scores of 0.951 and 0.952 were obtained,representing improvements of 0.144 and 0.276,respectively,compared to the original algorithm.